语义问题与人工智能模型构造的系统观点

基于"物理符号系统假设"的传统人工智能采信了低阶结构不连续的思想,将概念化与概念的语义基础分离,把对思维过程的模拟看作是能用形式化方法来实现的。但事实证明,这种后验性方法存在理论和实现上的双重危机,要完成有实际意义的、有创新性的智能行为丰富的语义是必需的,这使语义问题成为人工智能不同应用分支中的焦点,它包括语义获取、表达和使用三个方面。而要实现对语义问题的认识和解决就必须把它和其它智能行为作为一个连续的、相关的、不可分割的认知结构进行完整地考察,以系统的观点来看待智能模型的构造问题。这个认知结构统一性的基石就是基于神经生理基础的、以知觉的心理生理学解释为依据的、对语义的直接表达,这为统一以神经系统动力学为模型的其它各种智能行为提供了基础。     

Tacit and accessible understanding of language

The empirical nature of our understanding of language is explored. I first show that there are several important and different distinctions between tacit and accessible awareness. I then present empirical evidence concerning our understanding of language. The data suggests that our awareness of sentence-meanings is sometimes merely tacit according to one of these distinctions, but is accessible according to another. I present and defend an interpretation of this mixed view. The present project is shown to impact on several diverse areas, including inferential role semantics and holism, the nature of learning, and the role of linguistics in the law. I am indebted to a number of people for their useful feedback, especially Peter Ludlow, Paul Pietroski, and two anonymous reviewers. Earlier versions of this paper were presented at an Eastern meeting of the APA, a meeting of the Society for Exact Philosophy at Simon Fraser University, and at a semantics workshop in Ottawa, Canada. I greatly appreciate the comments from those audiences.     

Why philosophy and history matter: A conversation with Ann Taves

The article picks up some ideas that Ann Taves presents in her book Religious Experience Reconsidered, and looks at possible conversations that are not fleshed out in detail in Taves’ book. In particular, it is argued that the disciplinary confrontation with philosophy and with historiography is of crucial importance if the disciplines of cognitive science and psychology of religion want to become in the future what they pretend to be now—a serious alternative and complement to the study of religion as we know it from other contexts, such as cultural studies and historiography.     

Why philosophy and history matter: A conversation with Ann Taves

The article picks up some ideas that Ann Taves presents in her book Religious Experience Reconsidered, and looks at possible conversations that are not fleshed out in detail in Taves' book. In particular, it is argued that the disciplinary confrontation with philosophy and with historiography is of crucial importance if the disciplines of cognitive science and psychology of religion want to become in the future what they pretend to be nowda serious alternative and complement to the study of religion as we know it from other contexts, such as cultural studies and historiography     

Probability, logic and the cognitive foundations of rational belief

Since Pascal introduced the idea of mathematical probability in the 17th century discussions of uncertainty and “rational” belief have been dogged by philosophical and technical disputes. Furthermore, the last quarter century has seen an explosion of new questions and ideas, stimulated by developments in the computer and cognitive sciences. Competing ideas about probability are often driven by different intuitions about the nature of belief that arise from the needs of different domains (e.g., economics, management theory, engineering, medicine, the life sciences etc). Taking medicine as our focus we develop three lines of argument (historical, practical and cognitive) that suggest that traditional views of probability cannot accommodate all the competing demands and diverse constraints that arise in complex real-world domains. A model of uncertain reasoning based on a form of logical argumentation appears to unify many diverse ideas. The model has precursors in informal discussions of argumentation due to Toulmin, and the notion of logical probability advocated by Keynes, but recent developments in artificial intelligence and cognitive science suggest ways of resolving epistemological and technical issues that they could not address.     

Uncertainty about the rest of the sentence

A word-by-word human sentence processing complexity metric is presented. This metric formalizes the intuition that comprehenders have more trouble on words contributing larger amounts of information about the syntactic structure of the sentence as a whole. The formalization is in terms of the conditional entropy of grammatical continuations, given the words that have been heard so far. To calculate the predictions of this metric, Wilson and Carroll's (1954) original entropy reduction idea is extended to infinite languages. This is demonstrated with a mildly context-sensitive language that includes relative clauses formed on a variety of grammatical relations across the Accessibility Hierarchy of Keenan and Comrie (1977). Predictions are derived that correlate significantly with repetition accuracy results obtained in a sentence-memory experiment (Keenan & Hawkins, 1987).     

Sir Karl, Sir Ronald, (Sir) Paul, and the human element in the progress of soft psychology

Meehl’s article related to theoretical risks and tabular asterisks reminds us of the importance of thinking and theorizing as well as hypothesizing and testing, a point that is as relevant today in soft psychology as it was in 1978. Although progress is impeded by the profusion of tabular asterisks, we should recognize as well that the course of events is shaped by the actors involved—theories not only fade away, they are excluded by these actors.     

Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non‐neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.     

A proposed universal model of problem solving for design,science and cognate fields

A modestly generic, innovative, problem solving process with roots in the study of design and scientific research problem solving is presented and motivated. It is argued to be the shared core process of all problem solving. At its heart is a recognition of five foci or nodes of change vital to the process (changes in problem and solution formulation, method, constraints, and partial solution proposals) together with a bootstrap marked by the formation of higher order knowledge about problem solving in the domain in tandem with the solving of specific problems, the essential feature of all learned improvement. None of these elements is entirely original, but the way they are made explicit and developed (rather than folded into fewer, more abstract, boxes) is argued to provide fresh understanding of the organisation and power of the process to deal with complex practical problems.     

Investigating the relationship of working memory tasks and fluid intelligence tests by means of the fixed-links model in considering the impurity problem

The impurity of measures is considered as cause of erroneous interpretations of observed relationships. This paper concentrates on impurity with respect to the relationship between working memory and fluid intelligence. The means for the identification of impurity was the fixed-links model, which enabled the decomposition of variance into experimental and non-experimental parts. A substantial non-experimental part could be expected to signify impurity. In a sample of 345 participants error scores and reaction times, which were obtained by the Exchange Test, represented working memory, and Advanced Progressive Matrices served as measure of fluid intelligence. The four independent latent variables of the model associated with error scores and reaction times led to a multiple correlation .67 with the latent variable of fluid intelligence. However, there was impurity since the decomposition by means of the fixed-links model showed that only 45% of the common variance was due to working memory.     

Scaffolding the design of accessible eLearning content: a user-centered approach and cognitive perspective

There exist various guidelines for facilitating the design, preparation, and deployment of accessible eLearning applications and contents. However, such guidelines prevalently address accessibility in a rather technical sense, without giving sufficient consideration to the cognitive aspects and issues related to the use of eLearning materials by learners with disabilities. In this paper we describe how a user-centered design process was applied to develop a method and set of guidelines for didactical experts to scaffold their creation of accessible eLearning content, based on a more sound approach to accessibility. The paper also discusses possible design solutions for tools supporting eLearning content authors in the adoption and application of the proposed approach.